Methods and systems for evaluating bone lesions

ABSTRACT

Methods and systems for evaluating bone lesions include accessing a first dataset acquired from a patient with a first imaging modality and a second dataset acquired from the patient with a second imaging modality. A segmentation is performed on the first dataset to identify a subset of the first dataset corresponding to a skeletal structure of the patient and a patient skeletal metric representing a total bone volume of the patient is automatically calculated from the subset of the first dataset. The methods and systems further include detection of at least one lesion in the second dataset, classification of the at least one lesion as a bone or non-bone lesion, automatic calculation of a bone lesion metric based on the classification, and calculation of a lesion burden as a ratio of the bone lesion metric and the patient skeletal metric.

RELATED APPLICATION

This patent arises from a continuation of U.S. application Ser. No.14/013,087 (Now U.S. patent Ser. No. ______), entitled “METHODS ANDSYSTEMS FOR EVALUATING BONE LESIONS”, which was filed on Aug. 29, 2013and is hereby incorporated herein by reference in its entirety.

BACKGROUND OF THE INVENTION

Embodiments of the invention relate generally to medical diagnostics andtreatment assessment and, more particularly, to methods and systems forevaluating bone lesions using multi-modality image data.

It is not uncommon for a single patient to undergo a multitude ofmedical imaging exams, whether in a single doctor's visit, in a hospitalstay, or even over the course of a lifetime. This is particularly likelywhen a patient undergoes a series of “tests” and scans to investigate arecently onset or previously undetected condition. It is increasinglycommon for a patient to be subjected to multiple scans across multiplemedical imaging modalities because each exam can provide differentpieces of information. For example, during a single doctor's visit orhospital stay, a magnetic resonance (MR) imaging system, an x-rayimaging system, or a computed tomography (CT) imaging system can be usedto acquire images that provide anatomical information, while a positronemission tomography (PET) imaging system or functional MRI can be usedto acquire images that provide functional information. The anatomicalinformation providing insight into the anatomical makeup of the patientand the functional information providing insight into the functionalityof a given anatomical structure, especially when subjected to astimulus. Moreover, the combination of anatomical and functionalinformation is not only advantageous in detecting a new pathology orabnormality, but the respective images, when taken over the course of anillness, for example, may show growth of lesions, responses totreatments, and disease progression. To assist in the analysis ofanatomical and functional information, programs have been constructedthat register an anatomical and a functional image thereby showing, in asingle image, both anatomical and functional information.

Many clinical applications analyze 2D or 3D image data to perform andcapture quantitative analytics. These include detection and sizing oflung nodules (Advanced Lung Analysis), quantification of vesselcurvature, diameter, and tone (Advanced Vessel Analysis), cardiacvascular and function applications, navigating of the colon fordetection of polyps (CT colonography), detection and sizing of lesions,and the like. Dedicated CT, MR, PET and nuclear medicine applicationshave been designed to output quantitative analytics from regions ofinterest (intensity, density (HU), standard uptake value (SUV),distances, volumes, growth rates, pattern and/or texture recognition,functional information, etc.) to help in the diagnosis and management ofpatients.

Quantification of bone lesions using medical images is an importantaspect of clinical diagnostics and therapy. Information related to thequantity or overall volume of bone lesions detected in a patient may beused by medical practitioners to select the best course of treatment fora patient and to monitor treatment efficiency and collect relevantresearch data.

Conventional methods of quantifying bone lesions in a patient utilize areference value as an estimate of the volume of a particular patient'sskeletal structure. Because this reference value is a quantitative valueselected from tabular reference data based on the demographics (e.g.,age, sex, height, etc.) of the patient, the reference value may notaccurately reflect the actual skeletal volume of the patient.

Lesions within the skeletal structure of the patient are detected byfirst manually segmenting the skeletal structure from image dataacquired from the patient and manually identifying lesions within thesegmentation. The manual segmentation of the skeletal structural andmanual detection of lesions is very challenging due to the complexity ofthe skeletal structure. For example, the anatomy and composition of bonematerial can vary significantly among patients, which can lead tosignificant inter-operator variability.

Further, the segmentation of the skeletal structure typically includesthe brain, which can cause inaccurate quantitative analytics of theskeletal structure, and in turn affects a practitioner's ability toefficiently and accurately acquire measurements and data to assess thepatient's condition.

Therefore, it would be desirable to have a system and method ofquantifying bone lesions that overcomes the aforementioned drawbacks.

BRIEF DESCRIPTION OF THE INVENTION

In accordance with one aspect of the present invention, a non-transitorycomputer readable storage medium has stored thereon a computer programand represents a set of instructions that when executed by the computercauses the computer to access a first set of data of a first data typeand access a second set of data of a second data type. The first set ofdata is acquired from a patient with a first imaging modality, and thesecond set of data is acquired from the patient with a second imagingmodality. The set of instructions also causes the computer to perform asegmentation on the first set of data to identify a subset of the firstset of data corresponding to a skeletal structure of the patient.Further, the set of instructions causes the computer to automaticallycalculate a patient skeletal metric from the subset of the first set ofdata, the patient skeletal metric representing a total bone volume ofthe patient. In addition, the set of instructions causes the computer todetect at least one lesion in the second set of data, classify the atleast one lesion as a one of a bone lesion and a non-bone lesion, andautomatically calculate a bone lesion metric based on theclassification. The set of instructions further causes the computer tocalculate a lesion burden as a ratio of the bone lesion metric and thepatient skeletal metric.

According to another aspect of the invention, a method includesaccessing an anatomical image data set acquired from a patient andaccessing a function image data set acquired from the patient. Themethod also includes identifying a subset of the anatomical imagedataset corresponding to bone and calculating a skeletal volume from thesubset of the anatomical image dataset. In addition, the method includesidentifying a set of lesions from the function image dataset, comparingthe set of lesions to the subset of the anatomical image dataset toidentify at least one bone lesion within the set of lesions,automatically calculating a bone lesion burden based on the at least onebone lesion. Further, the method includes calculating a bone lesionindex from the bone lesion burden and the skeletal volume.

In accordance with another aspect of the invention, a medical diagnostictool includes an image acquisition system configured to acquiremulti-modality image data, a first database having stored thereon afirst image dataset acquired from a patient using a first imagingmodality, a second database having stored thereon a second image datasetacquired from the patient using a second imaging modality, and acomputer processor. The computer processor is programmed to access thefirst and second image datasets. The computer processor is furtherprogrammed to segment the first image dataset to define a set ofskeletal data, identify a location of a lesion candidate in the secondimage set, compare the location of the lesion candidate to the set ofskeletal data, and classify the lesion candidate as one of a bone lesionand a non-bone lesion based on the comparison to define a set of bonelesion data. In addition, the computer processor is programmed tocalculate a lesion burden based on a ratio of the set of bone lesiondata and the set of skeletal data. The medical diagnostic tool alsoincludes a graphical user interface (GUI) constructed to display animage generated from overlaying a first image corresponding to the setof skeletal data on a second image corresponding to the set of bonelesion data.

Various other features and advantages will be made apparent from thefollowing detailed description and the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings illustrate preferred embodiments presently contemplated forcarrying out the invention.

In the drawings:

FIG. 1 is a schematic diagram of a medical imaging system according toone embodiment of the invention.

FIG. 2 is a schematic block diagram of an exemplary multi-modalitymedical imaging system, useable with the medical imaging system of FIG.1, which includes a PET imaging system and a MR imaging system.

FIG. 3 is a schematic block diagram of an exemplary multi-modalitymedical imaging system, useable with the medical imaging system of FIG.1, which includes a PET imaging system and a CT imaging system.

FIG. 4 is a flowchart setting forth the steps of a technique forquantifying bone lesions using multi-modality image data according toone embodiment of the invention.

FIG. 5 is a flowchart setting forth the steps of a bone segmentationsubroutine of the technique of FIG. 4 according to one embodiment of theinvention.

FIG. 6 is a flowchart setting forth the steps of a lesion detectionsubroutine of the technique of FIG. 4 according to one embodiment of theinvention.

FIG. 7 illustrates an exemplary visual representation of a graphicaluser interface (GUI) for displaying a visualization of data inaccordance with one embodiment of the present invention.

DETAILED DESCRIPTION

The operating environment of embodiments of the invention is describedbelow with respect to a multi-modality imaging system that includes apositron emission tomography (PET) imaging system and one of an magneticresonance (MR) imaging system and a sixty-four-slice, “third generation”computed tomography (CT) imaging system. However, it will be appreciatedby those skilled in the art that the invention is equally applicable foruse with other multi-slice configurations and with other CT imagingsystems. In addition, while embodiments of the invention are describedwith respect to techniques for use with MR or CT imaging systems and PETimaging systems, one skilled in the art will recognize that the conceptsset forth herein are not limited to CT and PET and can be applied totechniques used with other imaging modalities in both the medical fieldand non-medical field, such as, for example, an x-ray system, asingle-photon emission computed tomography (SPECT) imaging system, orany combination thereof.

Referring now to FIG. 1, an exemplary imaging system 10 for use withembodiments of the present invention is shown. The imaging system 10 isnot modality specific. In this regard, the system 10 includes a centralprocessor (CPU) 12 that controls operation of the system 10. The imagingsystem 10 further includes a graphical user interface (GUI) 14 thatallows an operator to prescribe a scan and interact with the collecteddata and images. Data acquisition is controlled, via the CPU 12, by dataacquisition subsystem 16. According to various embodiments, dataacquisition subsystem 16 may access previously acquired data stored indatabase 18 or may acquire data directly from a multi-modality imagingsubsystem 20. Multi-modality imaging subsystem 20 may be a combinedPET/MR system (as shown in FIG. 2), a combined PET/CT system (as shownin FIG. 3), a combined SPECT/MR system, or a combined SPECT/CT system,as non-limiting examples.

Imaging system 10 further includes an image processing andreconstruction subsystem 22 that generates reconstructed images as wella quantitative data analysis subsystem 24 that derives quantitative datafrom data collected from data acquisition system 16. The reconstructedimages generated by image processing and reconstruction subsystem 22 andthe quantitative data computed or otherwise derived from quantitativedata analysis subsystem 24 may be stored in database 18. Additionally,it is contemplated that database 18 may be more than one database andremotely located from the site of data acquisition.

System 10 further has one or more displays 26 to visually display imagesand quantitative data as set forth herein. One having skill in the artwill appreciate that that system 10 may include other software,firmware, and hardware not specifically described to prescribe andexecute a given scan, as well as processing the data for imagereconstruction and quantitative analysis.

Additionally, it is contemplated that a dedicated workstation 28 havinga computer, monitor(s), and operably connected to the one or moredatabases may be used such that a physician may analyze the image andquantitative data remote from the scanner. As such, it is understoodthat systems and methods described herein may be used remotely from thetreatment facility at which the patient is scanned.

FIG. 2 illustrates an exemplary PET/MR imaging system 30 useable withmulti-modality imaging subsystem 20. The operation of PET/MR imagingsystem 30 may be controlled from an operator console 32, which includesa keyboard or other input device 34, a control panel 36, and a display38. The console 32 communicates though a link 40 with a separatecomputer system 42 that enable an operator to control the production anddisplay of images on the display 38. The computer system 42 includes anumber of modules, such as an image processor module 44, a CPU module46, and a memory module 48. The computer system 42 may also be connectedto permanent or back-up memory storage, a network, or may communicatewith a separate system control 50 through link 52. The input device 34can include a mouse, keyboard, track ball, touch activated screen, lightwand, or any similar or equivalent input device, and may be used forinteractive geometry prescription.

The system control 50 includes a set of modules in communication withone another and connected to the operator console 32 through link 54. Itis through link 52 that the system control 50 receives commands toindicate the scan sequence or sequences that are to be performed. For MRdata acquisition, an RF transmit/receive module 56 commands a scanner 58to carry out the desired scan sequence, by sending instructions,commands, and/or requests describing the timing, strength, and shape ofthe RF pulses and pulse sequences to be produced, to correspond to thetiming and length of the data acquisition window. In this regard, atransmit/receive switch 60 controls the flow of data via an amplifier 62to scanner 58 from RF transmit module 56 and from scanner 58 to RFreceive module 56. The system control 50 also connects to a set ofgradient amplifiers 64, to indicate the timing and shape of the gradientpulses that are produced during the scan.

The gradient waveform instructions produced by system control 50 aresent to the gradient amplifier system 64 having Gx, Gy, and Gzamplifiers. Amplifiers 64 may be external of scanner 58 or systemcontrol 50, or may be integrated therein. Each gradient amplifierexcites a corresponding physical gradient coil in a gradient coilassembly 66 to produce the magnetic field gradients used for spatiallyencoding acquired signals. The gradient coil assembly 66 forms part of amagnet assembly 68, which includes a polarizing magnet 70 and an RF coilassembly 72, 74. Alternatively, the gradient coils of gradient coilassembly 66 may be independent of the magnet assembly 68. RF coilassembly may include a whole-body RF transmit coil 72 as shown, surfaceor parallel imaging coils 74, or a combination of both. The coils 72, 74of the RF coil assembly may be configured for both transmitting andreceiving, or for transmit-only or receive-only. A pulse generator 76may generate PET data blanking signals synchronously with the productionof the pulse sequences. These blanking signals may be generated onseparate logic lines for subsequent data processing. The MR signalsresulting from the excitation pulses, emitted by the excited nuclei inthe patient, may be sensed by the whole body coil 72 or by separatereceive coils, such as parallel coils or surface coils 74, and are thentransmitted to the RF transmit/receive module 56 via thetransmit/receive switch 60. The MR signals are demodulated, filtered,and digitized in a data processing section 78 of the system control 50.

An MR scan is complete when one or more sets of raw k-space data hasbeen acquired in the data processor 78. This raw k-space data isreconstructed in data processor 78, which operates to transform the data(through Fourier or other techniques) in image data. This image data isconveyed through link 52 to the computer system 42, where it is storedin memory 48. Alternatively, in some systems computer system 42 mayassume the image data reconstruction and other functions of dataprocessor 78. In response to commands received from the operator console32, the image data stored in memory 48 may be archived in long termstorage or may be further processed by the image processor 44 or CPU 46and conveyed to the operator console 32 and presented on the display 38.

In combined PET/MR imaging systems, PET data may be acquiredsimultaneously with the MR data acquisition described above. Thus,scanner 58 also contains a positron emission detector 80, configured todetect gamma rays from positron annihilations emitted from a subject.Detector 80 preferably includes a plurality of scintillators andphotovoltaics arranged about a gantry. Detector 80 may, however, be ofany suitable construction for acquiring PET data. Gamma ray incidencesdetected by detector 80 are transformed, by the photovoltaics of thedetector 80, into electrical signals and are conditioned by a series offront-end electronics 82. These conditioning circuits 82 may includesvarious amplifies, filters, and analog-to-digital converters. Thedigital signals output by front-end electronics 82 are then processed bya coincidence processor 84 to match gamma ray detections as potentialcoincidence events. When two gamma rays strike detectors approximatelyopposite one another, it is possible, absent the interactions of randomnoise and signal gamma ray detections, that a positron annihilation tookplace somewhere along the line between the detectors. Thus, thecoincidences determined by coincidence processor 84 are sorted into truecoincidence events and are ultimately integrated by data sorter 86. Thecoincidence event data, or PET data, from data sorter 86 is received bythe system control 50 at a PET data receive port 88 and stored in memory48 for subsequent processing 78. PET images may then be reconstructed byimage processor 44 and may be combined with MR images to produce hybridstructural and metabolic or functional images. Front-end electronics 82,coincidence processor 84, and data sorter 86 may each be external ofscanner 58 or system control 50, or may be integrated therein.

A blanking control or monitor 90 is also included in system control 50.Blanking monitor 90 identified and records times during which MRcomponents 66-72 are active or transmitting. Blanking monitor 90 may usethis timing data to gate PET data acquisition by detector 80 or signalconditioning by front-end electronics 82, or may output a timingsequence to be applied during data processing by coincidence processor84, data sorter 86, data processor 78, or image reconstructor 44.

Referring now to FIG. 3, a PET/CT imaging system 92, useable withmulti-modality imaging subsystem 20 of FIG. 1 is shown. It should benoted that in some embodiments, PET/CT imaging system 92 acquires CTdata prior to obtaining the PET data. However, one skilled in the artwill recognize that the PET and CT data may be acquired in differentorders and combinations thereof (e.g., in an interleaved manner).

PET/CT imaging system 92 generally includes a gantry 94, a patient table96, and a processing and control system 98 including a user input 100with a display 102. The gantry 94 provides mechanical support forimaging devices such as, for example, detectors, scanners, andtransmitters that are used for scanning a patient 104. The gantry 94houses imaging devices such as, for example, PET detectors or x-raydetectors. It should be noted that the PET system may be a stationaryannular detector or optionally may include a pin source.

In some embodiments, gantry 94 includes a plurality of PET detectorsthat are fixed and spaced on the gantry 94, which are positionedradially outward from the axis. In accordance with other embodiments,the gantry 94 includes a plurality of detectors that are rotatable aboutthe axis. For CT imaging, a rotating detectors and a source, forexample, an x-ray tube 106 may be provided and optionally including astationary detector ring for CT imaging may be provided. In otherembodiments, a separate imaging gantry is provided for CT imaging.

The imaging devices on the gantry 94 acquire image data by scanning thepatient 104 lying on the patient table 96. The patient table 96 liesalong the axis of the gantry 94, and can be moved along this axis.Moving the patient table 96 enables the scanning of various portions ofthe patient 104. The patient table 96 can be positioned at various axialpositions along the axis.

The processing and control system 98 controls the positioning of thepatient table 96, and receives image data collected during scanning. Invarious embodiments, the processing and control system 98 controls thePET/CT imaging system 92 to acquire both emission and transmission imagedata of a volume of interest. For example, annihilation events may bedetected as emission data, as well as transmission data from signaltransmitted by a transmission source, such as the x-ray tube 106, whichpass through the volume of interest of the patient 104. The transmissionsignals may get attenuated when the signals pass through the volume ofinterest and the detectors may collect data that is attenuated after thetransmission signals pass through the patient 104.

Various processors, sorters, and databases are used to acquire andmanipulate emission and transmission data, which is used in accordancewith various embodiments. The processors, sorters, and databases of FIG.3 include acquisition circuitry 108, an acquisition processor 110, atransmission data database 112, an emission data database 114, and animage reconstruction processor 116. The acquisition processor 110 isprogrammed to acquire emission data and generate an image based on theemission data acquired. Other computing components also may be included.

In some embodiments, an energy sorter 118 provides, for example, time,location, and energy data to a PET processor 120. The PET processor 120generally uses the received data to identify pairs of data, also knownas coincidence pairs, coincident pair lines, and lines of response,corresponding to annihilation events that occurred inside the region ofinterest. After acquisition processor 110 identifies an annihilationevent, the acquisition processor 110 updates data in the emission datadatabase 114 to store information relating to the annihilation event. CTdata is also stored in the transmission data database 112 based ontransmission signals that pass through the patient 104 and are detected.

Thus, after an acquisition session has been completed and sets oftransmission and emission data have been stored in databases 112 and114, respectively, image reconstruction processor 116 accesses the datain the databases 112 and 114 and uses the accessed data to generateimages that may be requested by a system operator. Additionally, thesets of transmission and emission data are used by a scatter correctionestimator module 122 to estimate scatter for the emission data based onboth the transmission and emission data.

FIG. 4 sets forth a technique 200 for quantifying bone lesions in apatient using multi-modality three dimensional image data. According tovarious embodiments, technique 200 is performed by a computer processorsuch as, for example, CPU 12 of FIG. 1. Technique 200 begins with step202, by selecting and loading one or more exams to be processed by thecomputer system corresponding to one or more imaging sessions duringwhich multi-modality image data was acquired from a patient of interest.The multi-modality image data includes at least one set of anatomicalimage data acquired from the patient using an anatomical imagingmodality, such as CT or MR for example, and at least one set offunctional image data acquired from the patent using a functionalimaging modality, such as PET or SPECT as examples. In the exemplaryembodiment described below wherein the multi-modality image dataincludes three-dimensional CT and PET data, one CT series and one PETseries from one of the loaded exams is selected in step 204. Inalternative embodiments, one MR series and one PET series, one CT seriesand one SPECT series, and/or one MR series and one SPECT series may beselected from the loaded exams in step 204.

In optional step 206 (shown in phantom) technique 200 performs aregistration of the image data of the CT and PET series in order toaccurately overlay the anatomical and functional image data.Alternatively, in embodiments where the multi-modality image data isacquired from a combined PET/CT or a combined PET/MR imaging system, theacquired multi-modality image data may be automatically registered.

During step 208 an automatic bone segmentation is performed on theanatomical image data (e.g., data corresponding to the CT volume) usinga bone segmentation subroutine 210 described in detail below withrespect to FIG. 5. In one embodiment, a patient skeletal imagerepresenting the skeleton of the patient is generated from a subset ofthe anatomical image data during step 208 based on the automatic bonesegmentation.

Referring to FIG. 5, the bone segmentation subroutine 210 of technique200 is described in detail. As shown, bone segmentation subroutine 210begins at step 212 by accessing the anatomical image series, such as aCT series, selected at step 204 of technique 200. In optional step 214(shown in phantom) one or more views of the accessed image series isdisplayed on a user interface, such as on workstation 28 of FIG. 1, topermit a user to review the image series and adjust the segmentationparameters. In step 216 bone segmentation subroutine 210 performs anautomated segmentation to identify and define the skeletal structurewithin the anatomical image series using predefined segmentationparameters and/or the adjusted segmentation parameters defined in step214.

In embodiments where the anatomical image series includes CT data, bonesegmentation subroutine 210 uses a thresholding technique to segment theimage data. The thresholding technique may, for example, define imagedata having attenuation values within a predetermined range ascorresponding to skeletal or bone material. In embodiments where theanatomical image series includes MR data, bone segmentation subroutine210 segments the image data based on general CT anatomy atlas mapping orusing ultrashort echo time sequences generated MR data, where with athresholding algorithm the skeleton of the bone structure can beobtained. While various segmenting techniques are described herein, onehaving skill in the art will recognize that embodiments of the inventionmay segment different anatomical region bone structures usingalternative techniques, such as, for example, clustering, level set, andregion growing.

After performing the automated segmentation, bone segmentationsubroutine 210 fills the cavities on the defined bone structure to addthe bone marrow, the spinal-cord, and the brain in step 218. In step 220the jaw-bone is detected based on the bone/soft tissue ratio in order toeventually remove the brain. Bone segmentation subroutine 210 uses thedetected jaw-bone to separate the whole head and segment the brain instep 222. The segmented brain is removed from the anatomical image datain step 224. Bone segmentation subroutine 210 then generates a patientskeleton image from a subset of the anatomical image data representingthe bone of the patient and displays the image in one or more views atstep 226 for user review.

If the segmentation is deemed acceptable 228 at decision block 230, thebone segmentation subroutine 210 outputs the segmented skeletalstructure of the patient and ends at block 232. If the segmentation isnot acceptable 234, bone segmentation subroutine 210 proceeds todecision block 236 to determine whether the segmentation can becorrected by manual editing. If the segmentation cannot be corrected bymanual editing 238, bone segmentation subroutine 210 returns to step 214to adjust the segmentation parameters and restart the segmentationprocess. If the segmentation can be corrected by manual editing 240,bone segmentation subroutine 210) proceeds to step 242, wherein a usermanually edits the current results of the segmentation, such as bymodifying the contours of the resulting skeletal structure. After themanual editing procedure is complete, bone segmentation subroutine 210returns to decision block 230 to determine whether the result of theedited segmentation is acceptable.

Referring again to FIG. 4, after the bone segmentation subroutine 210 ofstep 208 is complete, technique 200 continues to step 244 whereintechnique 200 uses the segmented anatomical image data output from bonesegmentation subroutine 210 and the functional image data selected atstep 204 to detect bone lesions at using a lesion detection subroutine246, as described in detail in FIG. 6.

Referring now to FIG. 6, the lesion detection subroutine 246 begins atstep 248 by accessing the functional image data set corresponding to theimage series selected at step 204 of technique 200. In step 250 lesiondetection subroutine 246 automatically identifies possible lesioncandidates in the functional image data set using a thresholdingalgorithm. In one embodiment, the possible lesion candidates areidentified based on the standardize uptake values (SUV) of image voxelswithin the PET data series. For example, lesion candidates may bedefined as regions of voxels having a mean SUV within a predeterminedrange of values. During step 250, lesion detection subroutine 246 alsoassigns bookmarks to the lesion candidates. According to variousembodiments, the assigned bookmarks may be automatically assigned tolesion candidates by a computer processor, such as CPU 12 of FIG. 1,and/or manually assigned by a user. The bookmarks may includeidentifying information for the lesion candidates including for example,a unique lesion identifier and/or the positional coordinates for therespective lesion candidate.

Images of the lesion candidates are generated in step 252 and displayedas an overlay on the patient skeletal image generated from the segmentedskeletal structure output from bone segmentation subroutine 210 of FIG.5. In one embodiment, the segmented skeletal structure from a CT imageseries is projected into a PET image that includes the lesioncandidates. In decision block 254, the results of the automated lesioncandidate detection step are reviewed to determine if the result of thelesion candidate detection is acceptable. If the lesion detection is notacceptable 256, lesion detection subroutine 246 proceeds to step 258 inwhich the lesion detection parameters are adjusted. However, if thelesion detection is acceptable 260, lesion detection subroutine 246 isprompted to move on to step 262 and classify the identified lesioncandidates as being either bone lesions (i.e., lesions located withinthe skeletal structure) or non-bone lesions (i.e., lesions not locatedin the skeletal structure).

While step 252 is described above as occurring prior to decision block254, lesion detection subroutine 246 may perform step 252 after decisionblock 254 determines that the results of the lesion candidateidentification is acceptable in alternative embodiments.

To assist with classifying lesion candidates as being either bonelesions or non-bone lesions at step 262, lesion detection subroutine 246uses the segmented CT volume of step 208. In particular, lesiondetection subroutine 246 compares the location of the lesion candidatesto the location of the identified skeletal structure. Any lesioncandidates that fall within the location of the skeletal structure areautomatically identified as bone lesions and any lesion candidates thatare outside location of the skeletal structure are automaticallyidentified as non-bone lesions. Because lesion detection subroutine 246automatically classifies lesions as being bone or non-bone based on theidentified location of the lesion candidate with respect to theidentified location of the patient's skeletal structure, lesioncandidates may be correctly classified as being bone lesions with highaccuracy. The bookmark information for the lesion candidates may beupdated following lesion detection subroutine 246 to include informationto indicate whether the lesion candidate is a bone lesion or a non-bonelesion. Further, data corresponding to the classified bone lesions maybe combined and interpreted as a common object for quantitativeanalysis, rather than reviewing and determining quantitative data foreach lesion separately. Lesion detection subroutine 246 ends at block264.

Referring again to FIG. 4, the results of the lesion classificationdetermined during lesion detection subroutine 246, as well as thebookmark information for the lesion candidates, are optionally output toa database for storage at step 266 (shown in phantom). Technique 200also uses the results of the lesion classification in step 268 tocalculate quantitative information regarding the skeletal structure anddetected bone lesions of the patient. It is contemplated thatquantitative information regarding the skeletal structure and detectedbone lesions may be in regard to individual bone lesions or the total ofall bone lesions, according to various embodiments. The quantitativeinformation is displayed at step 270.

According to one embodiment, technique 200 calculates a patient skeletalmetric that represents a total skeletal volume of the patient, a bonelesion metric that represents a total bone lesion volume of the patient,and a bone lesion index or lesion burden at step 268. The total skeletalvolume is a quantitative value calculated based on the final bonesegmentation output from bone segmentation subroutine 210 and representsthe total volume of bone within the patient. The total bone lesionvolume is a quantitative value calculated based on a total bone volumeof the lesion candidates classified as bone lesions by lesion detectionsubroutine 246. According to one embodiment, the bone lesion index iscalculated as a ratio of the total bone lesion volume to the totalskeletal volume and is represented as a percentage. The bone lesionindex quantifies the amount of disease in the patient's skeletalstructure and represents a total tumor burden of the skeletal structure.

In some embodiments, portions of the selected anatomical and functionalimage series, visual representations of the lesion candidates, and thequantitative information calculated by technique 200 may be displayed ona graphical user interface (GUI) 272 as illustrated in FIG. 7. As shown,GUI 272 includes a menu display region 274, which contains an anatomicalimage selection menu 276, a functional image selection menu 278, andvarious command buttons 280. Anatomical image selection menu 276 andfunctional image selection menu 278 allow the user to select whichmulti-modality image series to load and analyze. According to variousembodiments, command buttons 280 allow the user to interact with selectsteps of technique 200 and subroutines 210, 246. For example, onecommand button 280 may permit a user to adjust a segmentation parameterof segmentation subroutine 210 at step 214 or adjust lesion detectionparameters of lesion detection subroutine 246 at step 258.

According to an exemplary embodiment, GUI 272 includes an image displayregion 282 that is separated into multiple windows in order to displaymultiple images. For example, image display 282 may include a firstwindow 284 that displays a CT image, a second window 286 that displays acorresponding PET image, and a third window 288 that displays the CTimage overlaid on the PET image. Fourth, fifth, and sixth windows 290,292, 294 include various views of the segmented skeletal volume obtainedfrom the CT image using bone segmentation subroutine 210 overlaid withcontours of lesion candidates 296, 298 detected using lesion detectionsubroutine 246. In alternative embodiments, image display region 282includes one or more selected images corresponding to the multi-modalityimage series and the detected lesions. Image display region 282 mayfurther be configured to display bone lesions and non-bone lesions indifferent colors or with different shading to visually distinguishdetected bone lesions from non-bone lesions. As one example, a lesioncandidate classified as a bone lesion, such as lesion candidate 296, maybe displayed with a red contour, and a lesion candidate classified as anon-bone lesion, such as lesion candidate 298, may be displayed with ablue contour. According to various embodiments, image display region 282may be configured as an interactive workspace to permit a user tointeract with the images to perform various tasks including, forexample, manually identify lesion candidates, delete an automaticallyidentified lesion, and/or change the classification of an automaticallydetected lesion, as non-limiting examples.

Analysis display region 300 includes a visual display of thequantitative or statistical data computed in step 268 of technique 200.This data can include the total skeleton volume of the patient, thetotal volume of bone lesions and non-bone lesions, the total bone lesionvolume (i.e., total tumor burden), the calculated bone lesion index, andother statistical information calculated during step 268. An informationdisplay region 302 is also located within in GUI 272 to provide the userwith patient and scan information including, for example, name, age, thedate of scans, and/or various parameters associated with the scans.

GUI 272 also includes a bookmark display region 304 that listsidentifying information for the lesion candidates flagged by either thecomputer system or the user. According to various embodiments, bookmarkdisplay region 304 may include a unique identifier for a particularlesion candidate, the coordinates for that particular lesion candidate,and whether the particular lesion candidate is a bone-lesion or anon-bone lesion.

Optionally, one or more of regions 274, 282, 300, 302, and 304 may beconfigured as a control panel to permit a user to input and/or selectdata through input fields, dropdown menus, etc. It is noted that thearrangement of GUI 272 is provided merely for explanatory purposes, andthat other GUI arrangements, field names, and visual outputs may takedifferent forms within the scope of various embodiments of theinvention. Additional display techniques may also include temperaturegauges, graphs, dials, font variations, annotations, and the like.

A technical contribution for the disclosed method and apparatus is thatit provides for a computer implemented system and method of detectingbone lesions using multi-modality image data.

One skilled in the art will appreciate that embodiments of the inventionmay be interfaced to and controlled by a computer readable storagemedium having stored thereon a computer program. The computer readablestorage medium includes a plurality of components such as one or more ofelectronic components, hardware components, and/or computer softwarecomponents. These components may include one or more computer readablestorage media that generally stores instructions such as software,firmware and/or assembly language for performing one or more portions ofone or more implementations or embodiments of a sequence. These computerreadable storage media are generally non-transitory and/or tangible.Examples of such a computer readable storage medium include a recordabledata storage medium of a computer and/or storage device. The computerreadable storage media may employ, for example, one or more of amagnetic, electrical, optical, biological, and/or atomic data storagemedium. Further, such media may take the form of, for example, floppydisks, magnetic tapes, CD-ROMs, DVD-ROMs, hard disk drives, and/orelectronic memory. Other forms of non-transitory and/or tangiblecomputer readable storage media not list may be employed withembodiments of the invention.

A number of such components can be combined or divided in animplementation of a system. Further, such components may include a setand/or series of computer instructions written in or implemented withany of a number of programming languages, as will be appreciated bythose skilled in the art. In addition, other forms of computer readablemedia such as a carrier wave may be employed to embody a computer datasignal representing a sequence of instructions that when executed by oneor more computers causes the one or more computers to perform one ormore portions of one or more implementations or embodiments of asequence.

In accordance with one embodiment of the present invention, anon-transitory computer readable storage medium has stored thereon acomputer program and represents a set of instructions that when executedby the computer causes the computer to access a first set of data of afirst data type and a second set of data of a second data type. Thefirst set of data is acquired from a patient with a first imagingmodality, and the second set of data is acquired from the patient with asecond imaging modality. The set of instructions also causes thecomputer to perform a segmentation on the first set of data to identifya subset of the first set of data corresponding to a skeletal structureof the patient. Further, the set of instructions causes the computer toautomatically calculate a patient skeletal metric from the subset of thefirst set of data, the patient skeletal metric representing a total bonevolume of the patient. In addition, the set of instructions causes thecomputer to detect at least one lesion in the second set of data,classify the at least one lesion as a one of a bone lesion and anon-bone lesion, and automatically calculate a bone lesion metric basedon the classification. The set of instructions further causes thecomputer to calculate a lesion burden as a ratio of the bone lesionmetric and the patient skeletal metric.

According to another embodiment of the invention, a method includesaccessing an anatomical image data set acquired from a patient andaccessing a function image data set acquired from the patient. Themethod also includes identifying a subset of the anatomical imagedataset corresponding to bone and calculating a skeletal volume from thesubset of the anatomical image dataset. In addition, the method includesidentifying a set of lesions from the function image dataset, comparingthe set of lesions to the subset of the anatomical image dataset toidentify at least one bone lesion within the set of lesions, andautomatically calculating a bone lesion burden based on the at least onebone lesion. Further, the method includes calculating a bone lesionindex from the bone lesion burden and the skeletal volume.

According to yet another embodiment of the invention, a medicaldiagnostic tool includes an image acquisition system configured toacquire multi-modality image data, a first database having storedthereon a first image dataset acquired from a patient using a firstimaging modality, a second database having stored thereon a second imagedataset acquired from the patient using a second imaging modality, and acomputer processor. The computer processor is programmed to access thefirst and second image datasets. The computer processor is furtherprogrammed to segment the first image dataset to define a set ofskeletal data, identify a location of a lesion candidate in the secondimage set, compare the location of the lesion candidate to the set ofskeletal data, and classify the lesion candidate as one of a bone lesionand anon-bone lesion based on the comparison to define a set of bonelesion data. In addition, the computer processor is programmed tocalculate a lesion burden based on a ratio of the set of bone lesiondata and the set of skeletal data. The medical diagnostic tool alsoincludes a graphical user interface (GUI) constructed to display animage generated from overlaying a first image corresponding to the setof skeletal data on a second image corresponding to the set of bonelesion data.

This written description uses examples to disclose the invention,including the best mode, and also to enable any person skilled in theart to practice the invention, including making and using any devices orsystems and performing any incorporated methods. The patentable scope ofthe invention is defined by the claims, and may include other examplesthat occur to those skilled in the art. Such other examples are intendedto be within the scope of the claims if they have structural elementsthat do not differ from the literal language of the claims, or if theyinclude equivalent structural elements with insubstantial differencesfrom the literal languages of the claims.

What is claimed is:
 1. A non-transitory computer readable storage mediumhaving a computer program stored thereon and representing a set ofinstructions that, when executed by a computer, causes the computer to:perform a segmentation on image data to identify a skeletal structureusing a defined segmentation parameter; fill cavities on the identifiedskeletal structure in the image data, the cavities corresponding to bonemarrow, a spinal cord, and a brain; detect a jaw-bone in the image databased on a ratio of bone to soft tissue; and remove the brain from theimage data based on the detected jaw-bone.
 2. The computer readablestorage medium of claim 1, wherein the set of instructions furthercauses the computer to display a segmented image of the skeletalstructure for review by a user to determine if the segmented image is anacceptable result of the segmentation.
 3. The computer readable storagemedium of claim 2, wherein, the set of instructions further causes thecomputer to determine if the segmented image is an acceptable result ofthe segmentation.
 4. The computer readable storage medium of claim 3,wherein, if the segmented image is not an acceptable result, the set ofinstructions further causes the computer to determine if the segmentedimage is to be edited manually.
 5. The computer readable storage mediumof claim 3, wherein, if the segmented image is not an acceptable result,the set of instructions further causes the computer to adjust thesegmentation parameter to perform the segmentation a second time.
 6. Thecomputer readable storage medium of claim 1, wherein the set ofinstructions further causes the computer to access the image data onwhich the segmentation is to be performed.
 7. The computer readablestorage medium of claim 6, wherein the set of instructions furthercauses the computer to display the image data to a user to enable theuser to adjust the segmentation parameter.
 8. The computer readablestorage medium of claim 1, wherein the set of instructions furthercauses the computer to detect possible lesion candidates based on athresholding algorithm.
 9. The computer readable storage medium of claim8, wherein the set of instructions further causes the computer toclassify the possible lesion candidates as bone lesions or non-bonelesions.
 10. A method comprising: performing a segmentation on imagedata to identify a skeletal structure using a defined segmentationparameter; filling cavities on the identified skeletal structure in theimage data, the cavities corresponding to bone marrow, a spinal cord,and a brain; detecting a jaw-bone in the image data based on a ratio ofbone to soft tissue; and removing the brain from the image data based onthe detected jaw-bone.
 11. The method of claim 10 further comprisingdisplaying, via a computer, a segmented image of the skeletal structurefor review by a user to determine if the segmented image is anacceptable result of the segmentation.
 12. The method of claim 10further comprising determining if the segmented image is an acceptableresult of the segmentation.
 13. The method of claim 12 furthercomprising, if the segmented image is not an acceptable result,determining if the segmented image is to be edited manually.
 14. Themethod of claim 12 further comprising, if the segmented image is not anacceptable result, adjusting the segmentation parameter prior toperforming the segmentation a second time.
 15. The method of claim 10further comprising accessing, using a computer, the image data on whichthe segmentation is to be performed.
 16. The method of claim 15 furthercomprising displaying, via the computer, the image data to a user toenable the user to adjust the segmentation parameter.
 17. The method ofclaim 10 further comprising detecting possible lesion candidates basedon a thresholding algorithm.
 18. The method of claim 17 furthercomprising classifying the possible lesion candidates as bone lesions ornon-bone lesions.
 19. An apparatus comprising: a computer processorprogrammed to: perform a segmentation on image data to identify askeletal structure using a defined segmentation parameter; fill cavitieson the identified skeletal structure in the image data, the cavitiescorresponding to bone marrow, a spinal cord, and a brain; detect ajaw-bone in the image data based on a ratio of bone to soft tissue; andremove the brain from the image data based on the detected jaw-bone. 20.The apparatus of claim 19 further comprising a graphical user interfaceof a computer to display a segmented image of the skeletal structure forreview by a user to determine if the segmented image is an acceptableresult of the segmentation.